基於LSTM-OCSVM方法於預診斷與健康管理模型－以塗佈機為例

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彭千晏(Chien-Yen Peng) 查詢紙本館藏

畢業系所

工業管理研究所

論文名稱

基於LSTM-OCSVM方法於預診斷與健康管理模型－以塗佈機為例
(Developing a Prognostics and Health Management Model Based on LSTM-OCSVM Approach – A Case Study of Coating Machine)

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摘要(中)

隨著科技迅速發展，現今大多工廠漸漸往工業4.0邁進，而穩定高效的生產效率、良好的產品品質與高穩定度的設備機具，皆是製造業在全球市場中脫穎而出的重要關鍵。過去在設備維護方法中使用被動維護的方式，此種方式不僅會造成產能下降更會導致機台壽命減少，以致於成本增加，因此近年來預測性維護成為主流方法，在設備上安裝感測器，並運用物聯網、雲端運算、數據分析等方式挖掘資料中有意義的資訊加以利用，在故障發生前提早發現異常並迅速維修，以改善過去被動維護方式的種種缺點。
本研究使用A公司所提供之塗佈機運轉數據，使用長短期記憶網路（Long Short-Term Memory, LSTM）及單類支持向量機（One-Class Support Vector Machines, OCSVM），兩種方法混和建立半監督式學習模型，欲達成預測性維護方法中的預診斷與健康管理（Prognostics and Health Management, PHM）。實驗結果評價指標準確率達99.27%、召回率達100%，以及F2-Score達81.83%，且預測模型相較於實際的張力異常紀錄能提前20秒檢測到異常徵兆，以利機台檢驗負責人能於機台故障前做出決策確保產線順暢，並降低因機台異常導致產品良率下降及機台壽命減短等問題。

摘要(英)

With the rapid development of technology, most factories nowadays are gradually moving towards Industry 4.0, and stable and efficient production efficiency, good product quality and high stability of equipment and machinery are all important keys for the manufacturing industry to stand out in the global market. In the past, passive maintenance was used in equipment maintenance methods, which not only caused a decrease in production capacity but also led to a reduction in machine life, resulting in increased costs. In order to improve the shortcomings of the past reactive maintenance methods, we use the information provided by Company A to provide the sensors.
This study, a semi-supervised learning model was built using a mixture of Long Short-Term Memory (LSTM) and One-Class Support Vector Machines (OCSVM) using the data provided by Company A for the operation of the coating machine, in order to achieve Prognostics and Health Management. The experimental results were evaluated as accuracy 99.27%, recall 100%, and F2-Score 81.83%. The prediction model can detect abnormal symptoms 20 seconds earlier than the actual tension abnormality records, so that the machine inspection personnel can make decisions before the machine failure to ensure smooth production line and reduce the problems of decreased product yield and shortened machine life caused by machine abnormality.

關鍵字(中)

★ 智慧製造
★ 預診斷與健康管理
★ 長短期記憶網路
★ 單類支持向量機

關鍵字(英)

★ Smart Manufacturing
★ Prognostics and Health Management
★ Long Short-Term Memory Network
★ One-Class Support vector machines

論文目次

摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vii
一、緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究流程與架構 2
二、文獻探討 5
2.1 工業4.0與虛實系統 5
2.2 預測性維護策略（Predictive Maintenance, PdM） 7
2.2.1 預診斷與健康管理架構 7
2.2.2 預測性維護的預測方法 8
2.3 機器學習（Machine Learning, ML） 10
2.4 深度學習（Deep Learning, DL） 12
2.5 文獻小節 15
三、研究方法 17
3.1 研究對象 17
3.2 問題定義 18
3.3 長短期記憶（Long Short-Term Memory） 19
3.3.1 激勵函數（Activation Function） 21
3.3.2 損失函數（Loss Function） 23
3.3.3 優化器（Optimizer） 24
3.4 支持向量機 (Support vector machines, SVM) 25
3.4.1 核函數（Kernel Function） 28
3.4.2 單類支持向量機（One-Class SVM, OCSVM） 28
3.5 評價指標（Evaluation Metrics） 30
四、實驗結果與分析 32
4.1 實驗環境與開發工具 32
4.2 資料說明及前處理 33
4.3 實驗設計 35
4.3.1 滑動窗口（Sliding Window） 35
4.3.2 神經元數量 37
4.3.3 模型建立 40
4.3.4 懲罰係數及核函數係數 41
4.4 實驗結果 42
4.5 實驗分析與評估 45
4.5.1 健康管理機制 46
4.5.2 模型效益比較 47
五、結論與未來研究方向 50
參考文獻 52

參考文獻

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指導教授

陳振明(Jen-Ming Chen)

審核日期

2022-7-11

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